The system frequency of a synchronous power system varies with the imbalance of energy supplied and the electrical energy consumed. When large generating blocks are lost, the system undergoes a frequency swing relative to the size of the loss. Limits imposed on the magnitude of frequency deviation† prevent system collapse. Operation of frequency responsive plant to control frequency, results in lower machine efficiencies. Changes to the generation mix on the British transmission system have occurred in the past ten years, when the response requirement was last reviewed. Future increased levels of wind turbines‡ will alter the operational characteristics of the system and warrant investigation. A process to optimise the response requirements while maintaining statutory limits on frequency deviation has been identified. The method requires suitable load and generator models to replicate transmission system performance. A value to substitute for current load sensitivity to frequency has been presented from empirical studies. Traditional coal fired generator models have been improved with additional functions to provide a comparable response with existing units. A novel combined cycle gas turbine model using fundamental equations and control blocks has also been developed. A doubly fed induction generator model, based on existing literature, has been introduced for representing wind turbine behaviour in system response studies. Validation of individual models and the complete system against historic loss events has established confidence in the method. A review of the current system with the dynamic model showed that current primary response requirements are inadequate. The secondary response requirements generally show a slight reduction in the holding levels. Simulations including extra wind generation have shown that there is potential to reduce the primary response requirement in the future. The secondary response requirements are maintained with added wind farms.

A dynamic MEA absorption process model has been developed to study the operability of this process in a dynamic fashion and to develop a control strategy to maintain the operation of the MEA scrubbing CO2 capture process in the presence of the external perturbations that may arise from the transient operation of the power plant. The novelty in this work is that a mechanistic model based on the conservation laws of mass and energy have been developed for the complete MEA absorption process. The model developed in this work was implemented in gPROMS. The process response of the key output variables to changes in the key input process variables, i.e., the flue gas flow rate and the reboiler heat duty, are presented and discussed in this study. In order to represent the actual operation of a power plant, the dynamic response of the MEA absorption process to a sinusoidal change in the flue gas flow rate was also considered in the present analysis. The mechanistic dynamic model was applied to develop a basic feedback control strategy. The implementation of a control strategy was tested by changing the operating conditions for the flue gas flow rate. The controlled variables, i.e., the percentage of CO2 absorbed in the absorber column and the reboiler temperature, were maintained around their nominal set point values by manipulating the valve stem positions, which determine the lean solvent feed flow rate at the top of the absorber column, and the reboiler heat duty, respectively. For the sinusoidal test, the amplitude of the oscillations observed for the controlled variables was smaller than those observed for the open-loop tests. This is because the variability of the controlled variables was transferred to the manipulated variable in the closed loop. The mechanistic dynamic model developed in this process can be potentially used as a practical tool that can provide insight regarding the dynamic operation of MEA absorption process. The model developed in this work can also be used as a basis to develop other studies related to the operability, controllability and dynamic flexibility of this process.

Este trabalho apresenta o estudo da dinâmica de uma instalação propulsora a vapor através da técnica da modelagem e simulação. O modelo matemático é obtido das Leis da Termodinâmica e dos conceitos básicos da mecânica dos fluídos e transferência de calor, e a simulação é efetuada em um computador digital. Inicialmente cada componente do ciclo é modelado individualmente, determinando-se as suas variáveis de estado, de entrada e de saída. A seguir o modelo é simulado para analisar a influência dos diversos parâmetros nas respostas do elemento. O modelo matemático da instalação propulsora a vapor é obtido agrupando-se convenientemente os modelos dos vários componentes do ciclo. Com isso obtêm-se um sistema de 47ª ordem que pode simular diversos casos de interesse real. Neste trabalho são analisadas as respostas do ciclo para dois casos. A primeira corresponde ao fechamento parcial da válvula de controle da turbina e a segunda ao corte na vazão de óleo combustível. / This paper presents a study of the dynamics of a steam power plant using techniques of modelling and simulation. The mathematical model derives form the laws of Thermodynamics and basic concepts of fluid mechanics and heat transfer; the simulation is carried out in a digital computer. Each component is first modelled individually, and its state, input and output variables are determined. The model is then simulated for the analysis of the influence of the various parameters in the responses of the component. The mathematical model of the complete power plant is constructed by the convenient grouping of the various component models of the cycle. Thereby a 47th order system is obtained, which can simulate various cases of interest. The cycle\'s response for two cases are analysed. The first case correspond to the partial closing of the turbine control valve and the second to the fuel flow interruption .

The aim of this thesis is to analyze and optimize production processes in a company which produces plastic components for the automotive industry. The theoretical part of this thesis summarizes basic knowledge of business process simulation and addresses the reasons for making use of dynamic simulation as a tool for eliminating dissipation and supporting decision making. The main focus is an analysis of the current situation and identifies the problems in the company. For the purpose of optimization, a simulation model was created using Simul8, by the Simul8 Corporation. The model was made in accordance with the methodology described in the theoretical part of this thesis. It was used to examine the efficiency of proposed versions of the production schedule, in light of its impact on storage capacity and service level.

Dynamic simulation of a large-scale electric power system involves solving a large number of differential algebraic equations (DAEs) every simulation time-step. With the ever-growing size and complexity of power grid, dynamic simulation becomes more and more time-consuming and computationally difficult using conventional sequential simulation techniques. This thesis presents a fully distributed approach intended for implementation on High Performance Computer (HPC) clusters. A novel, relaxation-based domain decomposition algorithm known as Parallel-General-Norton with Multiple-port Equivalent (PGNME) is proposed as the core technique of a two-stage decomposition approach to divide the overall dynamic simulation problem into a set of sub problems that can be solved concurrently. While the convergence property has traditionally been a concern for relaxation-based decomposition, an estimation mechanism based on multiple-port network equivalent is adopted as the preconditioner to enhance the convergence of the proposed algorithm. The algorithm is presented in detail and validated both in terms of accuracy and capability

The work of this thesis is concerned with the aggregation and advancement of modelling practise as used within modern day product development and optimisation environments making use of Model Based Design (¿MBD¿) and similar procedures. A review of model development and use forms the foundation of the work, with the findings being aggregated into two unique approaches for rapid model development and reconfiguration; the Plug-and-Simulate (¿PaS¿) approach and the Paradigm for Large Model Creation (¿PLMC¿); each shown to posses its own advantages. To support the MBD process a model optimisation algorithm that seeks to eliminate parameters that are of little or no significance to a simulation is developed. Eliminations are made on the basis of an energy analysis which determines the activity of a number of energy elements. Low activity elements are said to be of less significance to the global dynamics of a model and thus become targets for elimination. A model configuration tool is presented that brings together the PLMC and parameter elimination algorithm. The tool is shown to be useful for rapid configuration and reconfiguration of models and is capable of automatically running the optimisation algorithms thus producing a simulation model that is parametrically and computationally optimised. The response of the plug-and-simulate drivetrain submodels, assembled to represent a front wheel drive drivetrain, is examined. The resulting model is subjected to a torque step-input and an empirically obtained torque curve that characterises the input to a drivetrain undergoing steady acceleration. The model displays the expected response in both its full parameter and parameter reduced versions with simulation efficiency gains observed in the parameter reduced version. / EPSRC

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Proton exchange membrane fuel cells (PEMFCs) are one of the leading candidates in alternative energy conversion devices for transportation, stationary, and portable power generation applications. PEMFC systems with their own fuel conversion unit typically consist of several subsystems: a fuel processing subsystem, a fuel cell stack subsystem, a work recovery-air supply subsystem, and a power electronics subsystem. Since these subsystems have different physical characteristics, their integration into a single system/subsystem level unit make the problems of dynamic system synthesis/design and operation/control highly complex. Typically, the synthesis/design optimization of energy systems is based on a single full load condition at steady state. However, a more comprehensive synthesis/design and operation/control optimization requires taking into account part as well as full load conditions for satisfying an optimal efficiency/cost/environmental effect objective. Optimal couple of these various aspects of system development requires dynamic system/subsystem/component modeling and a multi-disciplinary approach which results in an integrated set of diverse types of models and highly effective optimization strategies such as decomposition techniques (e.g., Dynamic Iterative Local-Global Optimization: DILGO). In energy system synthesis/design and operation/control problems, system/ component models are typically treated deterministically, even though input values, which include the specific load profile for which the system or subsystem is developed, can have significant uncertainties that inevitably propagate through the system to the outputs. This deficiency can be overcome by treating the inputs and outputs probabilistically. In this work, various uncertainty analysis methodologies are applied; and among these traditional probabilistic approaches (e.g., Monte Carlo simulation) and the response sensitivity analysis (RSA) method are examined to determine their applicability to energy system development. In particular, these methods are used for the probabilistic (non-deterministic) modeling, analysis, and optimization of a residential 5 kWe PEMFC system, and uncertainty effects on the energy system synthesis/design and operation/control optimization have been assessed by taking the uncertainties into account in the objectives and constraints. Optimization results show that there is little effect on the objective (the operating cost and capital cost), while the constraints (e.g., on the CO concentration) can be significantly affected during the synthesis/design and operation/control optimization. / Ph. D.

The work of this thesis is concerned with the aggregation and advancement of modelling practise as used within modern day product development and optimisation environments making use of Model Based Design ('MBD') and similar procedures. A review of model development and use forms the foundation of the work, with the findings being aggregated into two unique approaches for rapid model development and reconfiguration; the Plug-and-Simulate ('PaS') approach and the Paradigm for Large Model Creation ('PLMC'); each shown to posses its own advantages. To support the MBD process a model optimisation algorithm that seeks to eliminate parameters that are of little or no significance to a simulation is developed. Eliminations are made on the basis of an energy analysis which determines the activity of a number of energy elements. Low activity elements are said to be of less significance to the global dynamics of a model and thus become targets for elimination. A model configuration tool is presented that brings together the PLMC and parameter elimination algorithm. The tool is shown to be useful for rapid configuration and reconfiguration of models and is capable of automatically running the optimisation algorithms thus producing a simulation model that is parametrically and computationally optimised. The response of the plug-and-simulate drivetrain submodels, assembled to represent a front wheel drive drivetrain, is examined. The resulting model is subjected to a torque step-input and an empirically obtained torque curve that characterises the input to a drivetrain undergoing steady acceleration. The model displays the expected response in both its full parameter and parameter reduced versions with simulation efficiency gains observed in the parameter reduced version.